Multivariate or multidimensional visualization plays an essential role in exploratory data analysis by allowing users to derive insights and formulate hypotheses. Despite their popularity, it is usually users' responsibility to (visually) discover the data patterns, which can be cumbersome and time-consuming. Visual Analytics (VA) and machine learning techniques can be instrumental in mitigating this problem by automatically discovering and representing such patterns. One example is the integration of classification models with (visual) interpretability strategies, where models are used as surrogates for data patterns so that understanding a model enables understanding the phenomenon represented by the data. Although useful and inspiring, the few proposed solutions are based on visual representations of so-called black-box models, so the interpretation of the patterns captured by the models is not straightforward, requiring mechanisms to transform them into human-understandable pieces of information. This paper presents multiVariate dAta eXplanation (VAX), a new VA method to support identifying and visual interpreting patterns in multivariate datasets. Unlike the existing similar approaches, VAX uses the concept of Jumping Emerging Patterns, inherent interpretable logic statements representing class-variable relationships (patterns) derived from random Decision Trees. The potential of VAX is shown through use cases employing two real-world datasets covering different scenarios where intricate patterns are discovered and represented, something challenging to be done using usual exploratory approaches.

Over the past decades, classification models have proven to be essential machine learning tools given their potential and applicability in various domains. In these years, the north of the majority of the researchers had been to improve quantitative metrics, notwithstanding the lack of information about models' decisions such metrics convey. This paradigm has recently shifted, and strategies beyond tables and numbers to assist in interpreting models' decisions are increasing in importance. Part of this trend, visualization techniques have been extensively used to support classification models' interpretability, with a significant focus on rule-based models. Despite the advances, the existing approaches present limitations in terms of visual scalability, and the visualization of large and complex models, such as the ones produced by the Random Forest (RF) technique, remains a challenge. In this paper, we propose Explainable Matrix (ExMatrix), a novel visualization method for RF interpretability that can handle models with massive quantities of rules. It employs a simple yet powerful matrix-like visual metaphor, where rows are rules, columns are features, and cells are rules predicates, enabling the analysis of entire models and auditing classification results. ExMatrix applicability is confirmed via different examples, showing how it can be used in practice to promote RF models interpretability.

Anatomy learning is very important for many courses, and it requires understanding three-dimensional (3D) shapes, most forms of anatomy teaching are offered to students through two-dimensional (2D) resources, such as images and videos. Many software solutions were developed with the purpose of assisting anatomy teaching and learning, arranging varied techniques and assets, as interaction and visualization of 3D virtual models. Despite great features, these solutions lack standards utilization and pedagogical methodology. This paper aims to present a 3D content generation technique to Moodle, one of the most popular platforms for teaching and learning management, built upon a specific pedagogical methodology, and validated by educational experts. The standard used in this technique allow sharing and reuse of content not only among Moodle systems, which are used by a large number of educational institutions and professors but also in other Virtual Learning Environments.

A process-scheduling algorithm is a fundamental operating system function that manages the assignment of CPU (Central Processing Unit) processes. It aims to make the system efficient, fast, and fair, allowing as many processes as possible to make the best use of the CPU at any given time. Understanding scheduling algorithms and their impact in practice is a challenging and time-consuming task for students, given the differing features of each operating system. Operating system simulators are usually based on either a command line or bidimensional front-end interface. This paper presents a process-scheduling simulator based on virtual reality technology that allows students to develop their empirical skills while learning process-scheduling algorithms. The simulator allows the learner to navigate inside a computer motherboard and interact with its components in multi-projection environments such as CAVE-like systems (Cave Automatic Virtual Environment), on a desktop, in a Web browser, or using an HMD (head-mounted display) such as Google Cardboard.

Software libraries allow developers to create software projects upon basic functions already implemented. In this way, it is possible to focus on more complex activities to achieve the software solution aims. Software libraries features and availability on the Internet are the reason for these valuable project resources are widely used. However, there may be some issues in software projects that integrate several libraries, since they are independent projects that must work together. This paper presents LibViews, an information visualization application to create visual representations over libraries metrics and usage on software projects. LibViews was developed to provide a better understanding of libraries versions and their role in software projects, helping in the maintenance of these projects identifying previously unknown information. As use case, LibViews was applied in an university corporate software project, pointing out as a useful tool to understand the relationship between software project and its third-party libraries.

Visualization applications can be performed on distinct platforms, such as mobile devices and multi-projection systems. Each platform offers specific features to provide further data understanding, and a system that integrates these platforms in a complementary manner is a real challenge. In this paper, we present an immersive and interactive visualization system that aims to explore data from relational databases using 3D graphs representations, where multiple simultaneous users can visualize and interact with the data through a multi-projection system and mobile devices. A single visualization application was created for both platforms using the Unity game engine, and an Unity external package for Virtual Reality applications development, that supports multi-projection system over a PC cluster and passive stereoscopy. Our visualization system aims to provide the users a better data understanding using a 3-screens multi-projection system as data overview, and mobile devices as display and interaction device for navigation and additional information visualization. We also introduce an user case, where the visualization system is used in order to support developers regarding structural problems in a large relational database.